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Wenyu Liu
Researcher at Huazhong University of Science and Technology
Publications - 411
Citations - 23420
Wenyu Liu is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 56, co-authored 357 publications receiving 15312 citations. Previous affiliations of Wenyu Liu include Guangzhou Medical University & Cornell University.
Papers
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Proceedings ArticleDOI
CCNet: Criss-Cross Attention for Semantic Segmentation
TL;DR: CCNet as mentioned in this paper proposes a recurrent criss-cross attention module to harvest the contextual information of all the pixels on its crisscross path, and then takes a further recurrent operation to finally capture the full-image dependencies from all pixels.
Posted Content
Deep High-Resolution Representation Learning for Visual Recognition
Jingdong Wang,Ke Sun,Tianheng Cheng,Borui Jiang,Chaorui Deng,Yang Zhao,Dong Liu,Yadong Mu,Mingkui Tan,Xinggang Wang,Wenyu Liu,Bin Xiao +11 more
TL;DR: The superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, is shown, suggesting that the HRNet is a stronger backbone for computer vision problems.
Journal ArticleDOI
Deep High-Resolution Representation Learning for Visual Recognition
Jingdong Wang,Ke Sun,Tianheng Cheng,Borui Jiang,Chaorui Deng,Yang Zhao,Dong Liu,Yadong Mu,Mingkui Tan,Xinggang Wang,Wenyu Liu,Bin Xiao +11 more
TL;DR: The High-Resolution Network (HRNet) as mentioned in this paper maintains high-resolution representations through the whole process by connecting the high-to-low resolution convolution streams in parallel and repeatedly exchanging the information across resolutions.
Posted Content
CCNet: Criss-Cross Attention for Semantic Segmentation
TL;DR: This work proposes a Criss-Cross Network (CCNet) for obtaining contextual information in a more effective and efficient way and achieves the mIoU score of 81.4 and 45.22 on Cityscapes test set and ADE20K validation set, respectively, which are the new state-of-the-art results.
Proceedings ArticleDOI
Detecting texts of arbitrary orientations in natural images
TL;DR: A system which detects texts of arbitrary orientations in natural images using a two-level classification scheme and two sets of features specially designed for capturing both the intrinsic characteristics of texts to better evaluate its algorithm and compare it with other competing algorithms.